Overview

Brought to you by YData

Dataset statistics

Number of variables19
Number of observations248,245
Missing cells0
Missing cells (%)0.0%
Duplicate rows153
Duplicate rows (%)0.1%
Total size in memory36.0 MiB
Average record size in memory152.0 B

Variable types

Categorical10
Numeric8
DateTime1

Alerts

Dataset has 153 (0.1%) duplicate rowsDuplicates
active_days is highly overall correlated with ad_view_cntHigh correlation
ad_view_cnt is highly overall correlated with active_daysHigh correlation
city is highly overall correlated with postcodeHigh correlation
postcode is highly overall correlated with cityHigh correlation
property_subtype is highly imbalanced (59.3%) Imbalance
garden_access is highly imbalanced (78.5%) Imbalance
building_floor_count has 2987 (1.2%) zeros Zeros
balcony_area has 162843 (65.6%) zeros Zeros

Reproduction

Analysis started2025-03-30 20:03:41.151771
Analysis finished2025-03-30 20:03:49.713193
Duration8.56 seconds
Software versionydata-profiling vv4.14.0
Download configurationconfig.json

Variables

city
Categorical

High correlation 

Distinct23
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
Budapest XIV.
27583 
Budapest XIII.
27191 
Budapest XI.
21974 
Budapest VII.
19568 
Budapest III.
18069 
Other values (18)
133860 

Length

Max length15
Median length14
Mean length12.734198
Min length11

Characters and Unicode

Total characters3,161,201
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBudapest I.
2nd rowBudapest I.
3rd rowBudapest I.
4th rowBudapest I.
5th rowBudapest I.

Common Values

ValueCountFrequency (%)
Budapest XIV. 27583
11.1%
Budapest XIII. 27191
 
11.0%
Budapest XI. 21974
 
8.9%
Budapest VII. 19568
 
7.9%
Budapest III. 18069
 
7.3%
Budapest VIII. 17668
 
7.1%
Budapest VI. 14293
 
5.8%
Budapest IX. 12166
 
4.9%
Budapest IV. 10725
 
4.3%
Budapest II. 10329
 
4.2%
Other values (13) 68679
27.7%

Length

2025-03-30T22:03:49.746740image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
budapest 248245
50.0%
xiv 27583
 
5.6%
xiii 27191
 
5.5%
xi 21974
 
4.4%
vii 19568
 
3.9%
iii 18069
 
3.6%
viii 17668
 
3.6%
vi 14293
 
2.9%
ix 12166
 
2.5%
iv 10725
 
2.2%
Other values (14) 79008
 
15.9%

Most occurring characters

ValueCountFrequency (%)
I 396246
12.5%
u 248245
7.9%
d 248245
7.9%
a 248245
7.9%
B 248245
7.9%
p 248245
7.9%
e 248245
7.9%
t 248245
7.9%
s 248245
7.9%
248245
7.9%
Other values (3) 530750
16.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3161201
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
I 396246
12.5%
u 248245
7.9%
d 248245
7.9%
a 248245
7.9%
B 248245
7.9%
p 248245
7.9%
e 248245
7.9%
t 248245
7.9%
s 248245
7.9%
248245
7.9%
Other values (3) 530750
16.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3161201
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
I 396246
12.5%
u 248245
7.9%
d 248245
7.9%
a 248245
7.9%
B 248245
7.9%
p 248245
7.9%
e 248245
7.9%
t 248245
7.9%
s 248245
7.9%
248245
7.9%
Other values (3) 530750
16.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3161201
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
I 396246
12.5%
u 248245
7.9%
d 248245
7.9%
a 248245
7.9%
B 248245
7.9%
p 248245
7.9%
e 248245
7.9%
t 248245
7.9%
s 248245
7.9%
248245
7.9%
Other values (3) 530750
16.8%

postcode
Real number (ℝ)

High correlation 

Distinct162
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1104.5512
Minimum1011
Maximum1239
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2025-03-30T22:03:49.796292image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1011
5-th percentile1026
Q11066
median1108
Q31141
95-th percentile1201
Maximum1239
Range228
Interquartile range (IQR)75

Descriptive statistics

Standard deviation51.232663
Coefficient of variation (CV)0.046383239
Kurtosis-0.66538183
Mean1104.5512
Median Absolute Deviation (MAD)36
Skewness0.19755779
Sum2.7419932 × 108
Variance2624.7858
MonotonicityNot monotonic
2025-03-30T22:03:49.850375image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1141 13789
 
5.6%
1131 12205
 
4.9%
1111 10919
 
4.4%
1081 8738
 
3.5%
1071 8648
 
3.5%
1031 6441
 
2.6%
1041 6136
 
2.5%
1021 5210
 
2.1%
1101 4700
 
1.9%
1039 4529
 
1.8%
Other values (152) 166930
67.2%
ValueCountFrequency (%)
1011 1654
 
0.7%
1012 605
 
0.2%
1013 275
 
0.1%
1014 217
 
0.1%
1015 669
 
0.3%
1016 1087
 
0.4%
1021 5210
2.1%
1022 507
 
0.2%
1023 266
 
0.1%
1024 1160
 
0.5%
ValueCountFrequency (%)
1239 15
 
< 0.1%
1238 88
 
< 0.1%
1237 199
 
0.1%
1231 27
 
< 0.1%
1225 154
 
0.1%
1224 159
 
0.1%
1223 87
 
< 0.1%
1222 191
 
0.1%
1221 1285
0.5%
1215 63
 
< 0.1%

property_subtype
Categorical

Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
brick flat (for sale)
185735 
prefabricated panel flat (for sale)
62506 
terraced house
 
3
prefabricated panel flat (for rent)
 
1

Length

Max length35
Median length21
Mean length24.525054
Min length14

Characters and Unicode

Total characters6,088,222
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowbrick flat (for sale)
2nd rowbrick flat (for sale)
3rd rowbrick flat (for sale)
4th rowbrick flat (for sale)
5th rowbrick flat (for sale)

Common Values

ValueCountFrequency (%)
brick flat (for sale) 185735
74.8%
prefabricated panel flat (for sale) 62506
 
25.2%
terraced house 3
 
< 0.1%
prefabricated panel flat (for rent) 1
 
< 0.1%

Length

2025-03-30T22:03:49.901938image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-30T22:03:49.937488image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
flat 248242
23.5%
for 248242
23.5%
sale 248241
23.5%
brick 185735
17.6%
prefabricated 62507
 
5.9%
panel 62507
 
5.9%
terraced 3
 
< 0.1%
house 3
 
< 0.1%
rent 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
807236
13.3%
a 684007
11.2%
r 558998
 
9.2%
f 558991
 
9.2%
l 558990
 
9.2%
e 435772
 
7.2%
t 310753
 
5.1%
c 248245
 
4.1%
o 248245
 
4.1%
s 248244
 
4.1%
Other values (10) 1428741
23.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6088222
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
807236
13.3%
a 684007
11.2%
r 558998
 
9.2%
f 558991
 
9.2%
l 558990
 
9.2%
e 435772
 
7.2%
t 310753
 
5.1%
c 248245
 
4.1%
o 248245
 
4.1%
s 248244
 
4.1%
Other values (10) 1428741
23.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6088222
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
807236
13.3%
a 684007
11.2%
r 558998
 
9.2%
f 558991
 
9.2%
l 558990
 
9.2%
e 435772
 
7.2%
t 310753
 
5.1%
c 248245
 
4.1%
o 248245
 
4.1%
s 248244
 
4.1%
Other values (10) 1428741
23.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6088222
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
807236
13.3%
a 684007
11.2%
r 558998
 
9.2%
f 558991
 
9.2%
l 558990
 
9.2%
e 435772
 
7.2%
t 310753
 
5.1%
c 248245
 
4.1%
o 248245
 
4.1%
s 248244
 
4.1%
Other values (10) 1428741
23.5%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
3
122574 
4
60992 
2
35626 
1
29053 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters248,245
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row3
3rd row4
4th row2
5th row3

Common Values

ValueCountFrequency (%)
3 122574
49.4%
4 60992
24.6%
2 35626
 
14.4%
1 29053
 
11.7%

Length

2025-03-30T22:03:49.983544image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-30T22:03:50.015099image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
3 122574
49.4%
4 60992
24.6%
2 35626
 
14.4%
1 29053
 
11.7%

Most occurring characters

ValueCountFrequency (%)
3 122574
49.4%
4 60992
24.6%
2 35626
 
14.4%
1 29053
 
11.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 248245
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 122574
49.4%
4 60992
24.6%
2 35626
 
14.4%
1 29053
 
11.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 248245
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 122574
49.4%
4 60992
24.6%
2 35626
 
14.4%
1 29053
 
11.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 248245
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 122574
49.4%
4 60992
24.6%
2 35626
 
14.4%
1 29053
 
11.7%

property_floor
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9539487
Minimum0
Maximum6
Zeros1430
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2025-03-30T22:03:50.048231image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median3
Q34
95-th percentile5
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1120234
Coefficient of variation (CV)0.37645318
Kurtosis0.092219119
Mean2.9539487
Median Absolute Deviation (MAD)0
Skewness-0.29711332
Sum733303
Variance1.236596
MonotonicityNot monotonic
2025-03-30T22:03:50.081441image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
3 132036
53.2%
4 45737
 
18.4%
1 39260
 
15.8%
5 16257
 
6.5%
2 11862
 
4.8%
6 1663
 
0.7%
0 1430
 
0.6%
ValueCountFrequency (%)
0 1430
 
0.6%
1 39260
 
15.8%
2 11862
 
4.8%
3 132036
53.2%
4 45737
 
18.4%
5 16257
 
6.5%
6 1663
 
0.7%
ValueCountFrequency (%)
6 1663
 
0.7%
5 16257
 
6.5%
4 45737
 
18.4%
3 132036
53.2%
2 11862
 
4.8%
1 39260
 
15.8%
0 1430
 
0.6%

building_floor_count
Real number (ℝ)

Zeros 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.34117505
Minimum-1
Maximum4
Zeros2987
Zeros (%)1.2%
Negative136138
Negative (%)54.8%
Memory size1.9 MiB
2025-03-30T22:03:50.113503image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median-1
Q32
95-th percentile3
Maximum4
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.5939341
Coefficient of variation (CV)4.6718954
Kurtosis-1.2462284
Mean0.34117505
Median Absolute Deviation (MAD)0
Skewness0.58757459
Sum84695
Variance2.5406261
MonotonicityNot monotonic
2025-03-30T22:03:50.147528image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
-1 136138
54.8%
2 40484
 
16.3%
1 34349
 
13.8%
3 31632
 
12.7%
0 2987
 
1.2%
4 2655
 
1.1%
ValueCountFrequency (%)
-1 136138
54.8%
0 2987
 
1.2%
1 34349
 
13.8%
2 40484
 
16.3%
3 31632
 
12.7%
4 2655
 
1.1%
ValueCountFrequency (%)
4 2655
 
1.1%
3 31632
 
12.7%
2 40484
 
16.3%
1 34349
 
13.8%
0 2987
 
1.2%
-1 136138
54.8%

view_type
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
0.0
114263 
1.0
53924 
3.0
35065 
2.0
29204 
4.0
15789 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters744,735
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row2.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 114263
46.0%
1.0 53924
21.7%
3.0 35065
 
14.1%
2.0 29204
 
11.8%
4.0 15789
 
6.4%

Length

2025-03-30T22:03:50.185032image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-30T22:03:50.217115image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 114263
46.0%
1.0 53924
21.7%
3.0 35065
 
14.1%
2.0 29204
 
11.8%
4.0 15789
 
6.4%

Most occurring characters

ValueCountFrequency (%)
0 362508
48.7%
. 248245
33.3%
1 53924
 
7.2%
3 35065
 
4.7%
2 29204
 
3.9%
4 15789
 
2.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 744735
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 362508
48.7%
. 248245
33.3%
1 53924
 
7.2%
3 35065
 
4.7%
2 29204
 
3.9%
4 15789
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 744735
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 362508
48.7%
. 248245
33.3%
1 53924
 
7.2%
3 35065
 
4.7%
2 29204
 
3.9%
4 15789
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 744735
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 362508
48.7%
. 248245
33.3%
1 53924
 
7.2%
3 35065
 
4.7%
2 29204
 
3.9%
4 15789
 
2.1%

orientation
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
0
95637 
3
72494 
2
59097 
1
21017 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters248,245
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row2
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 95637
38.5%
3 72494
29.2%
2 59097
23.8%
1 21017
 
8.5%

Length

2025-03-30T22:03:50.258188image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-30T22:03:50.288717image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 95637
38.5%
3 72494
29.2%
2 59097
23.8%
1 21017
 
8.5%

Most occurring characters

ValueCountFrequency (%)
0 95637
38.5%
3 72494
29.2%
2 59097
23.8%
1 21017
 
8.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 248245
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 95637
38.5%
3 72494
29.2%
2 59097
23.8%
1 21017
 
8.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 248245
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 95637
38.5%
3 72494
29.2%
2 59097
23.8%
1 21017
 
8.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 248245
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 95637
38.5%
3 72494
29.2%
2 59097
23.8%
1 21017
 
8.5%

garden_access
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
none
239759 
yes
 
8486

Length

Max length4
Median length4
Mean length3.965816
Min length3

Characters and Unicode

Total characters984,494
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownone
2nd rownone
3rd rownone
4th rownone
5th rownone

Common Values

ValueCountFrequency (%)
none 239759
96.6%
yes 8486
 
3.4%

Length

2025-03-30T22:03:50.326255image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-30T22:03:50.351628image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
none 239759
96.6%
yes 8486
 
3.4%

Most occurring characters

ValueCountFrequency (%)
n 479518
48.7%
e 248245
25.2%
o 239759
24.4%
y 8486
 
0.9%
s 8486
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 984494
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 479518
48.7%
e 248245
25.2%
o 239759
24.4%
y 8486
 
0.9%
s 8486
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 984494
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 479518
48.7%
e 248245
25.2%
o 239759
24.4%
y 8486
 
0.9%
s 8486
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 984494
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 479518
48.7%
e 248245
25.2%
o 239759
24.4%
y 8486
 
0.9%
s 8486
 
0.9%

heating_type
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
2.0
132125 
1.0
71397 
0.0
34830 
3.0
 
6957
4.0
 
2936

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters744,735
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row2.0
3rd row1.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 132125
53.2%
1.0 71397
28.8%
0.0 34830
 
14.0%
3.0 6957
 
2.8%
4.0 2936
 
1.2%

Length

2025-03-30T22:03:50.381157image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-30T22:03:50.410695image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2.0 132125
53.2%
1.0 71397
28.8%
0.0 34830
 
14.0%
3.0 6957
 
2.8%
4.0 2936
 
1.2%

Most occurring characters

ValueCountFrequency (%)
0 283075
38.0%
. 248245
33.3%
2 132125
17.7%
1 71397
 
9.6%
3 6957
 
0.9%
4 2936
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 744735
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 283075
38.0%
. 248245
33.3%
2 132125
17.7%
1 71397
 
9.6%
3 6957
 
0.9%
4 2936
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 744735
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 283075
38.0%
. 248245
33.3%
2 132125
17.7%
1 71397
 
9.6%
3 6957
 
0.9%
4 2936
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 744735
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 283075
38.0%
. 248245
33.3%
2 132125
17.7%
1 71397
 
9.6%
3 6957
 
0.9%
4 2936
 
0.4%

elevator_type
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
none
137113 
yes
111132 

Length

Max length4
Median length4
Mean length3.5523294
Min length3

Characters and Unicode

Total characters881,848
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowyes
2nd rowyes
3rd rownone
4th rownone
5th rowyes

Common Values

ValueCountFrequency (%)
none 137113
55.2%
yes 111132
44.8%

Length

2025-03-30T22:03:50.451257image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-30T22:03:50.477654image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
none 137113
55.2%
yes 111132
44.8%

Most occurring characters

ValueCountFrequency (%)
n 274226
31.1%
e 248245
28.2%
o 137113
15.5%
y 111132
12.6%
s 111132
12.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 881848
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 274226
31.1%
e 248245
28.2%
o 137113
15.5%
y 111132
12.6%
s 111132
12.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 881848
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 274226
31.1%
e 248245
28.2%
o 137113
15.5%
y 111132
12.6%
s 111132
12.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 881848
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 274226
31.1%
e 248245
28.2%
o 137113
15.5%
y 111132
12.6%
s 111132
12.6%

room_cnt
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
1.0
141726 
2.0
93526 
3.0
 
11857
0.0
 
916
4.0
 
220

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters744,735
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 141726
57.1%
2.0 93526
37.7%
3.0 11857
 
4.8%
0.0 916
 
0.4%
4.0 220
 
0.1%

Length

2025-03-30T22:03:50.510021image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-30T22:03:50.538981image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 141726
57.1%
2.0 93526
37.7%
3.0 11857
 
4.8%
0.0 916
 
0.4%
4.0 220
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 249161
33.5%
. 248245
33.3%
1 141726
19.0%
2 93526
 
12.6%
3 11857
 
1.6%
4 220
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 744735
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 249161
33.5%
. 248245
33.3%
1 141726
19.0%
2 93526
 
12.6%
3 11857
 
1.6%
4 220
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 744735
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 249161
33.5%
. 248245
33.3%
1 141726
19.0%
2 93526
 
12.6%
3 11857
 
1.6%
4 220
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 744735
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 249161
33.5%
. 248245
33.3%
1 141726
19.0%
2 93526
 
12.6%
3 11857
 
1.6%
4 220
 
< 0.1%

small_room_cnt
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
0.0
143082 
1.0
75962 
2.0
27893 
3.0
 
1260
4.0
 
48

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters744,735
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row1.0
4th row0.0
5th row2.0

Common Values

ValueCountFrequency (%)
0.0 143082
57.6%
1.0 75962
30.6%
2.0 27893
 
11.2%
3.0 1260
 
0.5%
4.0 48
 
< 0.1%

Length

2025-03-30T22:03:50.576525image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-30T22:03:50.607234image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 143082
57.6%
1.0 75962
30.6%
2.0 27893
 
11.2%
3.0 1260
 
0.5%
4.0 48
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 391327
52.5%
. 248245
33.3%
1 75962
 
10.2%
2 27893
 
3.7%
3 1260
 
0.2%
4 48
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 744735
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 391327
52.5%
. 248245
33.3%
1 75962
 
10.2%
2 27893
 
3.7%
3 1260
 
0.2%
4 48
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 744735
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 391327
52.5%
. 248245
33.3%
1 75962
 
10.2%
2 27893
 
3.7%
3 1260
 
0.2%
4 48
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 744735
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 391327
52.5%
. 248245
33.3%
1 75962
 
10.2%
2 27893
 
3.7%
3 1260
 
0.2%
4 48
 
< 0.1%
Distinct568
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
Minimum2015-02-09 00:00:00
Maximum2016-08-29 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-03-30T22:03:50.652214image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T22:03:50.708520image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

property_area
Real number (ℝ)

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48.601382
Minimum5
Maximum70
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2025-03-30T22:03:50.750617image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile25
Q140
median50
Q360
95-th percentile70
Maximum70
Range65
Interquartile range (IQR)20

Descriptive statistics

Standard deviation12.67771
Coefficient of variation (CV)0.26085081
Kurtosis-0.76144877
Mean48.601382
Median Absolute Deviation (MAD)10
Skewness-0.21353866
Sum12065050
Variance160.72433
MonotonicityNot monotonic
2025-03-30T22:03:50.784953image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
50 38510
15.5%
55 38170
15.4%
45 27605
11.1%
35 27021
10.9%
60 25618
10.3%
65 23529
9.5%
40 20961
8.4%
30 18190
7.3%
70 15635
6.3%
25 10382
 
4.2%
Other values (4) 2624
 
1.1%
ValueCountFrequency (%)
5 31
 
< 0.1%
10 170
 
0.1%
15 536
 
0.2%
20 1887
 
0.8%
25 10382
 
4.2%
30 18190
7.3%
35 27021
10.9%
40 20961
8.4%
45 27605
11.1%
50 38510
15.5%
ValueCountFrequency (%)
70 15635
6.3%
65 23529
9.5%
60 25618
10.3%
55 38170
15.4%
50 38510
15.5%
45 27605
11.1%
40 20961
8.4%
35 27021
10.9%
30 18190
7.3%
25 10382
 
4.2%

balcony_area
Real number (ℝ)

Zeros 

Distinct90
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0347358
Minimum0
Maximum96
Zeros162843
Zeros (%)65.6%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2025-03-30T22:03:50.832529image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile8
Maximum96
Range96
Interquartile range (IQR)3

Descriptive statistics

Standard deviation4.7490332
Coefficient of variation (CV)2.3339802
Kurtosis86.857593
Mean2.0347358
Median Absolute Deviation (MAD)0
Skewness7.1329109
Sum505113
Variance22.553316
MonotonicityNot monotonic
2025-03-30T22:03:50.883701image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 162843
65.6%
4 22867
 
9.2%
3 15612
 
6.3%
5 9335
 
3.8%
2 8145
 
3.3%
6 7861
 
3.2%
8 3750
 
1.5%
7 3745
 
1.5%
1 2438
 
1.0%
10 2182
 
0.9%
Other values (80) 9467
 
3.8%
ValueCountFrequency (%)
0 162843
65.6%
1 2438
 
1.0%
2 8145
 
3.3%
3 15612
 
6.3%
4 22867
 
9.2%
5 9335
 
3.8%
6 7861
 
3.2%
7 3745
 
1.5%
8 3750
 
1.5%
9 1624
 
0.7%
ValueCountFrequency (%)
96 16
 
< 0.1%
90 43
< 0.1%
88 5
 
< 0.1%
87 5
 
< 0.1%
86 1
 
< 0.1%
85 4
 
< 0.1%
84 5
 
< 0.1%
83 10
 
< 0.1%
82 15
 
< 0.1%
81 1
 
< 0.1%

price_created_at
Real number (ℝ)

Distinct574
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.380841
Minimum0.1
Maximum60
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2025-03-30T22:03:50.935098image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile9.1
Q113.3
median16.9
Q323.9
95-th percentile36.5
Maximum60
Range59.9
Interquartile range (IQR)10.6

Descriptive statistics

Standard deviation8.6301052
Coefficient of variation (CV)0.44529054
Kurtosis1.9311099
Mean19.380841
Median Absolute Deviation (MAD)4.6
Skewness1.2755812
Sum4811196.9
Variance74.478716
MonotonicityNot monotonic
2025-03-30T22:03:50.982983image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14.9 5600
 
2.3%
12.9 5327
 
2.1%
13.9 5276
 
2.1%
15.9 4698
 
1.9%
13.5 4560
 
1.8%
19.9 4502
 
1.8%
16.9 3941
 
1.6%
12.5 3940
 
1.6%
18.9 3749
 
1.5%
11.9 3585
 
1.4%
Other values (564) 203067
81.8%
ValueCountFrequency (%)
0.1 1
 
< 0.1%
0.2 2
 
< 0.1%
0.5 2
 
< 0.1%
0.6 1
 
< 0.1%
0.7 1
 
< 0.1%
0.8 2
 
< 0.1%
0.9 1
 
< 0.1%
1 7
< 0.1%
1.3 1
 
< 0.1%
1.5 2
 
< 0.1%
ValueCountFrequency (%)
60 31
 
< 0.1%
59.9 106
< 0.1%
59.8 40
 
< 0.1%
59.7 2
 
< 0.1%
59.6 3
 
< 0.1%
59.5 8
 
< 0.1%
59.4 2
 
< 0.1%
59.3 1
 
< 0.1%
59.2 11
 
< 0.1%
59.1 5
 
< 0.1%

ad_view_cnt
Real number (ℝ)

High correlation 

Distinct3835
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean243.44106
Minimum0
Maximum40248
Zeros22
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2025-03-30T22:03:51.030186image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile13
Q140
median97
Q3246
95-th percentile930
Maximum40248
Range40248
Interquartile range (IQR)206

Descriptive statistics

Standard deviation508.28378
Coefficient of variation (CV)2.0879131
Kurtosis499.34869
Mean243.44106
Median Absolute Deviation (MAD)70
Skewness12.993082
Sum60433027
Variance258352.4
MonotonicityNot monotonic
2025-03-30T22:03:51.087026image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24 1978
 
0.8%
17 1970
 
0.8%
25 1963
 
0.8%
28 1946
 
0.8%
18 1945
 
0.8%
22 1938
 
0.8%
26 1934
 
0.8%
23 1931
 
0.8%
20 1926
 
0.8%
19 1908
 
0.8%
Other values (3825) 228806
92.2%
ValueCountFrequency (%)
0 22
 
< 0.1%
1 82
 
< 0.1%
2 204
 
0.1%
3 335
 
0.1%
4 493
 
0.2%
5 655
0.3%
6 906
0.4%
7 1079
0.4%
8 1196
0.5%
9 1347
0.5%
ValueCountFrequency (%)
40248 1
< 0.1%
36988 1
< 0.1%
30012 1
< 0.1%
28096 1
< 0.1%
22574 1
< 0.1%
21410 1
< 0.1%
20277 1
< 0.1%
19592 1
< 0.1%
18914 1
< 0.1%
17668 1
< 0.1%

active_days
Real number (ℝ)

High correlation 

Distinct441
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.219912
Minimum1
Maximum544
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2025-03-30T22:03:51.146362image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q111
median28
Q361
95-th percentile141
Maximum544
Range543
Interquartile range (IQR)50

Descriptive statistics

Standard deviation47.944076
Coefficient of variation (CV)1.0842192
Kurtosis7.2197882
Mean44.219912
Median Absolute Deviation (MAD)20
Skewness2.2345011
Sum10977372
Variance2298.6344
MonotonicityNot monotonic
2025-03-30T22:03:51.199484image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 7049
 
2.8%
6 6509
 
2.6%
7 6291
 
2.5%
2 6243
 
2.5%
3 6080
 
2.4%
5 5962
 
2.4%
4 5717
 
2.3%
8 5421
 
2.2%
14 5188
 
2.1%
13 4779
 
1.9%
Other values (431) 189006
76.1%
ValueCountFrequency (%)
1 7049
2.8%
2 6243
2.5%
3 6080
2.4%
4 5717
2.3%
5 5962
2.4%
6 6509
2.6%
7 6291
2.5%
8 5421
2.2%
9 4649
1.9%
10 4517
1.8%
ValueCountFrequency (%)
544 1
 
< 0.1%
540 1
 
< 0.1%
537 1
 
< 0.1%
535 1
 
< 0.1%
528 3
< 0.1%
520 1
 
< 0.1%
511 1
 
< 0.1%
510 1
 
< 0.1%
487 1
 
< 0.1%
485 2
< 0.1%

Interactions

2025-03-30T22:03:48.604129image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T22:03:45.161386image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T22:03:45.664541image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T22:03:46.154699image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T22:03:46.632956image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T22:03:47.124184image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T22:03:47.602272image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T22:03:48.090535image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T22:03:48.663378image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T22:03:45.222577image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T22:03:45.724340image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T22:03:46.214761image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T22:03:46.695322image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T22:03:47.184054image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T22:03:47.664748image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T22:03:48.155375image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T22:03:48.724098image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T22:03:45.280645image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T22:03:45.784614image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T22:03:46.272351image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T22:03:46.758711image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T22:03:47.245100image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T22:03:47.726259image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T22:03:48.219448image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-03-30T22:03:45.338439image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T22:03:45.843539image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T22:03:46.329958image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T22:03:46.817402image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T22:03:47.303310image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T22:03:47.785085image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T22:03:48.281516image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T22:03:48.844632image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T22:03:45.410047image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T22:03:45.903813image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T22:03:46.389303image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T22:03:46.876136image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T22:03:47.362409image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T22:03:47.847246image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T22:03:48.344059image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T22:03:48.902711image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T22:03:45.480661image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T22:03:45.965392image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T22:03:46.448516image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T22:03:46.937896image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T22:03:47.420234image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T22:03:47.907720image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T22:03:48.404878image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T22:03:48.966589image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T22:03:45.539485image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T22:03:46.025566image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T22:03:46.509379image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T22:03:46.996695image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T22:03:47.479644image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T22:03:47.966299image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T22:03:48.469176image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T22:03:49.032454image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T22:03:45.602963image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T22:03:46.095132image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T22:03:46.573729image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T22:03:47.062489image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T22:03:47.542661image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T22:03:48.032953image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T22:03:48.539330image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-03-30T22:03:51.245281image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
active_daysad_view_cntbalcony_areabuilding_floor_countcityelevator_typegarden_accessheating_typeorientationpostcodeprice_created_atproperty_areaproperty_condition_typeproperty_floorproperty_subtyperoom_cntsmall_room_cntview_type
active_days1.0000.5320.0030.0570.0210.0460.0620.0120.011-0.0080.0890.0670.006-0.0450.0420.0270.0110.034
ad_view_cnt0.5321.0000.0890.3690.0080.0080.0180.0090.0030.014-0.149-0.0110.012-0.0330.0030.0000.0000.015
balcony_area0.0030.0891.0000.1540.0490.0410.1580.0660.0340.1380.1940.2450.1160.1800.0620.0310.0270.058
building_floor_count0.0570.3690.1541.0000.1740.4810.3530.1780.0820.023-0.0690.0440.1200.1570.2910.0620.0750.341
city0.0210.0080.0490.1741.0000.2610.1140.1950.1030.9520.2220.1150.0990.1400.3070.0910.1580.191
elevator_type0.0460.0080.0410.4810.2611.0000.0790.3640.1750.1540.1460.1820.1170.4340.3580.0960.1290.190
garden_access0.0620.0180.1580.3530.1140.0791.0000.0360.0500.0840.0620.0370.0400.2480.0980.0050.0190.198
heating_type0.0120.0090.0660.1780.1950.3640.0361.0000.0570.1600.1490.1810.1770.1910.2690.0840.1250.130
orientation0.0110.0030.0340.0820.1030.1750.0500.0571.0000.0770.0470.0410.0640.0630.0790.0160.0310.083
postcode-0.0080.0140.1380.0230.9520.1540.0840.1600.0771.000-0.2810.0080.0780.0620.2160.0630.1310.163
price_created_at0.089-0.1490.194-0.0690.2220.1460.0620.1490.047-0.2811.0000.4820.1870.0160.1990.1640.0690.051
property_area0.067-0.0110.2450.0440.1150.1820.0370.1810.0410.0080.4821.0000.0400.1560.1410.3450.2130.106
property_condition_type0.0060.0120.1160.1200.0990.1170.0400.1770.0640.0780.1870.0401.0000.0540.0980.0210.0460.114
property_floor-0.045-0.0330.1800.1570.1400.4340.2480.1910.0630.0620.0160.1560.0541.0000.2850.0550.0820.174
property_subtype0.0420.0030.0620.2910.3070.3580.0980.2690.0790.2160.1990.1410.0980.2851.0000.0760.1650.141
room_cnt0.0270.0000.0310.0620.0910.0960.0050.0840.0160.0630.1640.3450.0210.0550.0761.0000.1980.049
small_room_cnt0.0110.0000.0270.0750.1580.1290.0190.1250.0310.1310.0690.2130.0460.0820.1650.1981.0000.058
view_type0.0340.0150.0580.3410.1910.1900.1980.1300.0830.1630.0510.1060.1140.1740.1410.0490.0581.000

Missing values

2025-03-30T22:03:49.236108image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-03-30T22:03:49.423861image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

citypostcodeproperty_subtypeproperty_condition_typeproperty_floorbuilding_floor_countview_typeorientationgarden_accessheating_typeelevator_typeroom_cntsmall_room_cntcreated_atproperty_areabalcony_areaprice_created_atad_view_cntactive_days
0Budapest I.1015.0brick flat (for sale)24-1.00.01none3.0yes1.00.02016-01-2940.04.022.9128.016.0
1Budapest I.1012.0brick flat (for sale)332.02.02none2.0yes1.01.02016-04-1970.00.033.063.034.0
2Budapest I.1016.0brick flat (for sale)43-1.00.02none1.0none1.01.02015-12-0840.01.021.585.072.0
3Budapest I.1016.0brick flat (for sale)20-1.00.00none2.0none1.00.02015-06-1835.00.013.053.013.0
4Budapest I.1015.0brick flat (for sale)311.01.00none2.0yes1.02.02015-12-0855.00.033.5153.052.0
5Budapest I.1013.0brick flat (for sale)311.02.01none2.0none1.01.02015-11-0445.00.022.5753.090.0
6Budapest I.1012.0brick flat (for sale)23-1.01.00none1.0yes2.01.02015-03-2765.00.020.021.03.0
7Budapest I.1012.0brick flat (for sale)23-1.00.03none1.0yes3.00.02015-03-2765.00.020.041.04.0
8Budapest I.1012.0brick flat (for sale)331.01.03none2.0yes2.00.02015-06-0350.00.025.0142.011.0
9Budapest I.1016.0brick flat (for sale)16-1.00.00none0.0none1.00.02015-06-1040.00.013.9732.034.0
citypostcodeproperty_subtypeproperty_condition_typeproperty_floorbuilding_floor_countview_typeorientationgarden_accessheating_typeelevator_typeroom_cntsmall_room_cntcreated_atproperty_areabalcony_areaprice_created_atad_view_cntactive_days
248235Budapest XIV.1141.0brick flat (for sale)43-1.00.02none2.0none1.01.02016-08-2435.00.016.0126.02.0
248236Budapest XI.1111.0brick flat (for sale)34-1.00.03none2.0yes1.01.02016-08-2540.00.021.936.04.0
248237Budapest IV.1041.0prefabricated panel flat (for sale)353.04.03none0.0yes1.00.02016-08-2535.00.013.081.01.0
248238Budapest XI.1111.0brick flat (for sale)34-1.00.00none2.0yes1.01.02016-08-2540.00.021.920.01.0
248239Budapest XV.1151.0prefabricated panel flat (for sale)44-1.00.02none2.0yes1.01.02016-08-2635.00.012.510.04.0
248240Budapest XIII.1131.0brick flat (for sale)310.03.02yes1.0none2.00.02016-08-2650.00.015.0268.03.0
248241Budapest XII.1121.0brick flat (for sale)12-1.00.00none1.0none2.00.02016-08-2665.06.023.9187.04.0
248242Budapest XI.1111.0brick flat (for sale)43-1.00.02none0.0yes1.01.02016-08-2755.03.033.548.01.0
248243Budapest VIII.1081.0brick flat (for sale)234.00.02none0.0yes2.00.02016-08-2855.08.018.0115.03.0
248244Budapest XIV.1141.0brick flat (for sale)31-1.00.00none1.0none1.00.02016-08-2925.00.09.056.01.0

Duplicate rows

Most frequently occurring

citypostcodeproperty_subtypeproperty_condition_typeproperty_floorbuilding_floor_countview_typeorientationgarden_accessheating_typeelevator_typeroom_cntsmall_room_cntcreated_atproperty_areabalcony_areaprice_created_atad_view_cntactive_days# duplicates
0Budapest I.1011.0brick flat (for sale)312.02.03none2.0yes1.01.02015-03-0240.00.017.652.031.02
1Budapest I.1011.0brick flat (for sale)33-1.00.02none1.0yes1.00.02016-01-2735.00.023.917.013.02
2Budapest I.1015.0brick flat (for sale)33-1.01.01none2.0yes2.00.02015-10-1450.00.027.053.020.02
3Budapest II.1021.0brick flat (for sale)222.02.01none2.0yes1.01.02016-02-2160.00.025.934.02.02
4Budapest II.1021.0brick flat (for sale)34-1.00.03none2.0yes1.01.02016-07-0840.00.026.914.017.02
5Budapest III.1031.0brick flat (for sale)32-1.00.03none1.0none1.00.02016-06-2235.00.016.221.016.02
6Budapest III.1031.0prefabricated panel flat (for sale)44-1.00.03none2.0yes2.00.02016-05-0950.00.021.221.029.02
7Budapest III.1034.0brick flat (for sale)43-1.00.02none4.0yes1.00.02015-11-3045.05.025.78.017.02
8Budapest III.1034.0brick flat (for sale)43-1.00.02none4.0yes1.00.02016-04-0840.05.025.932.049.02
9Budapest III.1039.0prefabricated panel flat (for sale)23-1.00.00none2.0yes2.00.02016-05-2450.04.014.740.028.02